Company Overview

LockedIn AI is the #1 real-time AI interview and meeting copilot, trusted by over 1 million users worldwide. We are building a next-generation AI-powered career intelligence platform that helps users succeed in interviews, coding assessments, and professional communication.

Our system delivers real-time AI assistance at scale, requiring highly reliable, efficient, and continuously improving machine learning infrastructure.

Position: MLOps Engineer

Employment Type: Full-Time
Work Model: Remote (US-Based) with optional hybrid in New York, NY
Compensation: $140,000 – $200,000 USD per year
Reports To: Co-Founder / CEO

Role Overview

We are looking for a hands-on MLOps Engineer to own the end-to-end machine learning lifecycle at LockedIn AI.

In this role, you will act as the bridge between machine learning research and production engineering. Your primary responsibility is ensuring that all AI models powering our real-time copilot are deployed reliably, scaled efficiently, monitored continuously, and improved over time.

You will design and maintain the infrastructure, automation systems, and pipelines that enable fast, safe, and scalable ML deployments for over 1 million users.

Key Responsibilities

ML Deployment & Lifecycle Management

• Own the full lifecycle of ML models from development to production and retirement
• Build and maintain model serving infrastructure for real-time inference
• Manage model registries, versioning systems, and artifact tracking
• Implement deployment strategies including canary releases, shadow deployments, and rollbacks

ML CI/CD & Automation

• Design and maintain CI/CD pipelines for machine learning workflows
• Automate model training, validation, testing, packaging, and deployment
• Implement automated quality gates including performance, accuracy, and bias checks
• Build retraining pipelines triggered by drift detection or scheduled updates
• Manage ML orchestration tools such as MLflow, Kubeflow, Airflow, or Prefect

Monitoring, Observability & Drift Detection

• Build real-time monitoring systems for ML model performance
• Track latency, accuracy, cost, hallucination rate, and system health metrics
• Implement data drift and model drift detection systems
• Set up alerting systems with escalation workflows and remediation paths
• Create dashboards for engineering, product, and leadership visibility

Infrastructure & Cost Optimization

• Manage cloud infrastructure for training, inference, and model serving
• Optimize GPU and compute usage across workloads
• Design scalable containerized systems using Docker and Kubernetes
• Implement auto-scaling systems for variable inference workloads
• Track and optimize infrastructure and LLM-related costs

Data Pipeline & Feature Systems

• Build automated data pipelines for ingestion, transformation, and validation
• Ensure high-quality datasets for training and inference workflows
• Implement data versioning, lineage tracking, and reproducibility systems
• Collaborate on feature store design for ML consistency across environments
• Add validation and anomaly detection at every pipeline stage

Security, Compliance & Governance

• Ensure privacy-first ML infrastructure aligned with product standards
• Implement secure access control, encryption, and secrets management
• Maintain audit logs and model traceability systems
• Protect ML systems from adversarial inputs and infrastructure misuse
• Support governance frameworks for model lifecycle management

Required Qualifications

Experience

• 3+ years of experience in MLOps, ML engineering, or DevOps with ML systems
• Experience building end-to-end ML pipelines in production
• Hands-on experience with ML deployment and monitoring systems
• Experience working with cross-functional ML and engineering teams
• Startup or fast-paced environment experience preferred

Technical Skills

• Strong Python programming skills
• Experience with ML frameworks (PyTorch, TensorFlow, or similar)
• Experience with ML orchestration tools (MLflow, Kubeflow, Airflow, Prefect)
• Strong Docker and Kubernetes experience
• Experience with AWS, GCP, or Azure ML infrastructure
• CI/CD tools such as GitHub Actions, Jenkins, or ArgoCD
• Infrastructure as Code tools like Terraform or CloudFormation
• Monitoring tools like Prometheus, Grafana, Arize, or Evidently AI

Preferred Qualifications

• Experience with LLM infrastructure and real-time inference systems
• Knowledge of model optimization and cost reduction techniques
• Experience with GPU clusters and distributed training systems
• Familiarity with ML governance and compliance frameworks
• Experience with high-scale consumer AI products
• Contributions to open-source MLOps tools or systems
• Background in edtech, SaaS, or AI-first startups

What We Offer

• Competitive salary with meaningful early-stage equity
• Direct impact on a product used by over 1 million users
• Remote-first work culture with optional collaboration in New York
• High ownership of ML infrastructure and systems
• Fast-paced environment focused on execution and innovation
• Strong technical growth opportunities in applied AI systems

Why Join LockedIn AI

• Category-defining real-time AI copilot product
• Rapidly scaling global user base
• Full ownership of ML infrastructure powering production AI systems
• Work at the cutting edge of applied machine learning operations
• Opportunity to build systems that directly impact millions of users

How to Apply

Please submit:

• Resume or CV
• Short note including:
  • Why you want to join LockedIn AI
  • Whether you have used the product
  • Suggestions for improvement
• Optional: GitHub profile, portfolio, or technical writing samples